1,334 research outputs found
Self-Tuned Deep Super Resolution
Deep learning has been successfully applied to image super resolution (SR).
In this paper, we propose a deep joint super resolution (DJSR) model to exploit
both external and self similarities for SR. A Stacked Denoising Convolutional
Auto Encoder (SDCAE) is first pre-trained on external examples with proper data
augmentations. It is then fine-tuned with multi-scale self examples from each
input, where the reliability of self examples is explicitly taken into account.
We also enhance the model performance by sub-model training and selection. The
DJSR model is extensively evaluated and compared with state-of-the-arts, and
show noticeable performance improvements both quantitatively and perceptually
on a wide range of images
Score Function Features for Discriminative Learning: Matrix and Tensor Framework
Feature learning forms the cornerstone for tackling challenging learning
problems in domains such as speech, computer vision and natural language
processing. In this paper, we consider a novel class of matrix and
tensor-valued features, which can be pre-trained using unlabeled samples. We
present efficient algorithms for extracting discriminative information, given
these pre-trained features and labeled samples for any related task. Our class
of features are based on higher-order score functions, which capture local
variations in the probability density function of the input. We establish a
theoretical framework to characterize the nature of discriminative information
that can be extracted from score-function features, when used in conjunction
with labeled samples. We employ efficient spectral decomposition algorithms (on
matrices and tensors) for extracting discriminative components. The advantage
of employing tensor-valued features is that we can extract richer
discriminative information in the form of an overcomplete representations.
Thus, we present a novel framework for employing generative models of the input
for discriminative learning.Comment: 29 page
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